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dc.contributor.authorMartin, Keith
dc.contributor.authorMansouri, Kaweh
dc.contributor.authorWeinreb, Robert N
dc.contributor.authorWasilewicz, Robert
dc.contributor.authorGisler, Christophe
dc.contributor.authorHennebert, Jean
dc.contributor.authorGenoud, Dominique
dc.contributor.authorResearch Consortium
dc.date.accessioned2018-11-16T00:30:22Z
dc.date.available2018-11-16T00:30:22Z
dc.date.issued2018-10
dc.identifier.issn0002-9394
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285117
dc.description.abstractPURPOSE: To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open-angle glaucoma (POAG) and healthy (H) eyes. DESIGN: Development and evaluation of a diagnostic test with machine learning. METHODS: Subjects: From 435 subjects (193 healthy and 242 POAG), 136 POAG and 136 age-matched healthy subjects were selected. Subjects with contraindications for CLS wear were excluded. PROCEDURE: This is a pooled analysis of data from 24 prospective clinical studies and a registry. All subjects underwent 24-hour CLS recording on 1 eye. Statistical and physiological CLS parameters were derived from the signal recorded. CLS parameters frequently associated with the presence of POAG were identified using a random forest modeling approach. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (ROC AUC) for feature sets including CLS parameters and Start IOP, as well as a feature set with CLS parameters and Start IOP combined. RESULTS: The CLS parameters feature set discriminated POAG from H eyes with mean ROC AUCs of 0.611, confidence interval (CI) 0.493-0.722. Larger values of a given CLS parameter were in general associated with a diagnosis of POAG. The Start IOP feature set discriminated between POAG and H eyes with a mean ROC AUC of 0.681, CI 0.603-0.765. The combined feature set was the best indicator of POAG with an ROC AUC of 0.759, CI 0.654-0.855. This ROC AUC was statistically higher than for CLS parameters or Start IOP feature sets alone (both P < .0001). CONCLUSIONS: CLS recordings contain information complementary to IOP that enable discrimination between H and POAG. The feature set combining CLS parameters and Start IOP provide a better indication of the presence of POAG than each of the feature sets separately. As such, the CLS may be a new biomarker for POAG.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier BV
dc.subjectResearch Consortium
dc.subjectHumans
dc.subjectGlaucoma, Open-Angle
dc.subjectTonometry, Ocular
dc.subjectMonitoring, Ambulatory
dc.subjectTelemetry
dc.subjectArea Under Curve
dc.subjectProspective Studies
dc.subjectROC Curve
dc.subjectContact Lenses
dc.subjectIntraocular Pressure
dc.subjectAdult
dc.subjectAged
dc.subjectMiddle Aged
dc.subjectFemale
dc.subjectMale
dc.subjectMachine Learning
dc.titleUse of Machine Learning on Contact Lens Sensor-Derived Parameters for the Diagnosis of Primary Open-angle Glaucoma.
dc.typeArticle
prism.endingPage53
prism.publicationDate2018
prism.publicationNameAm J Ophthalmol
prism.startingPage46
prism.volume194
dc.identifier.doi10.17863/CAM.32488
dcterms.dateAccepted2018-07-11
rioxxterms.versionofrecord10.1016/j.ajo.2018.07.005
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-10
dc.contributor.orcidMartin, Keith [0000-0002-9347-3661]
dc.identifier.eissn1879-1891
rioxxterms.typeJournal Article/Review
rioxxterms.freetoread.startdate2019-10-31


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